Difference between revisions of "Stimulus tagging using aperiodic visual stimulation in a VEP-based BCI"

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=== Libraries used ===
 
=== Libraries used ===
 
[http://www.libsdl.org/ Simple Directmedia Layer]
 
[http://www.libsdl.org/ Simple Directmedia Layer]
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[http://www.opengl.org/ OpenGL]
  
<!-- === Roadmap ===
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=== Roadmap ===
 
# Video output
 
# Video output
## Specification
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## Specification - ''done''
## Design
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## Design - ''done''
## Implementation
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## Implementation - ''done''
## Testing
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## Testing - ''in progress''
# Classic SSVEP
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## Deployment - ''next''
## Specification/Design
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# Simple t-VEP
## Implementation
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## Specification/Design/Implementation - ''in progress''
## Data Acquisition
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## Data Acquisition - next
 
## Data Analysis
 
## Data Analysis
# Aperiodic Stimulus Tagging
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## Conclusions
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<!--# Aperiodic Stimulus Tagging
 
## Choice of Encoding
 
## Choice of Encoding
 
## Algorithm Adaptation / Design
 
## Algorithm Adaptation / Design
 
## Data Acquisition
 
## Data Acquisition
## Data Analysis & Feedback -->
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## Data Analysis & Feedback-->
  
 
=== Bibliography ===
 
=== Bibliography ===

Revision as of 11:15, 11 April 2010

Part 1: project profile

Project name

Aperiodic visual stimulation in a VEP-based BCI

Project short description

Visual-evoked potentials (VEPs) are a possible way to drive the a Brain-Computer Interface (BCI). This projects aims at maximizing the discrimination between different stimuli by using numerical codes derived from techniques of digital telecommunications.

Dates

Start date: 2009/10/01

People involved

Project head(s)

prof. Matteo Matteucci

Students currently working on the project

Giuseppe Broccio

Davide Castellone

Laboratory work and risk analysis

(work in progress) Several EEG sessions will be recorded. Data acquisition is performed at the IIT-Lab. All risks are those related to the use of video terminals.

Part 2: project description

What this project means to demonstrate is the possibility to extend the SSVEP paradigm to multiple visual signals (i.e. blinking lights) using a numeric encoding, in a way similar to what has already been attempted with noise-tagging.

Libraries used

Simple Directmedia Layer OpenGL

Roadmap

  1. Video output
    1. Specification - done
    2. Design - done
    3. Implementation - done
    4. Testing - in progress
    5. Deployment - next
  2. Simple t-VEP
    1. Specification/Design/Implementation - in progress
    2. Data Acquisition - next
    3. Data Analysis
    4. Conclusions


Bibliography

  • J.R. Wolpaw et al., Brain-computer interfaces for communication and control
  • Erich E. Sutter., The brain response interface: communication through visually-induced electrical brain responses
  • J. Farquhar, J. Blankespoor, R. Velk, P. Desain, Towards a noise tagging auditory BCI-Paradigm, in 4th International BCI Workshop and Training Course 2008